Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology

Authors : Umut ÖZKAYA

Pages : 248-255

View : 8 | Download : 9

Publication Date : 2023-01-31

Article Type : Research Paper

Abstract :In this study, a method based on Convolutional Neural Networks insert ignore into journalissuearticles values(CNN); and fusion technology was proposed for the classification of vital signals. In order to obtain more information from 1-D radar signals, 2-D data were obtained with the spectrogram technique. An automated classification framework has been implemented by using pre-trained Google Net, VGG-16 and ResNet-50 models. The performance in the test data is increased by applying late fusion process to the highest performing VGG-16 and GoogleNet CNN structures. The performance of the proposed method is 92.54% Accuracy insert ignore into journalissuearticles values(ACC);, 92.41% Sensitivity insert ignore into journalissuearticles values(SEN);, 97.18% Specificity insert ignore into journalissuearticles values(SPE);, 93.54% Precision insert ignore into journalissuearticles values(PRE);, 92.66% F1-Score, and 90.25% Matthews Correlation Constant insert ignore into journalissuearticles values(MCC);. Thanks to the proposed method, radar technology, which is one of the non-destructive detection technologies, comes to the forefront compared to wearable technologies
Keywords : Radar, Vital Sign, Deep Learning, Convolutional Neural Network, Late Fusion